Takes a bunch of reads, aligns them, and builds them back into two haplotypes.

Assumptions:

• There are no sequencing errors!
• There are only two haplotypes!

The algorithm uses the included directory structure for it's usuage of file-based storage. Removing elements from the folder directory structre (i.e. removing run_data or reference_genomes) will result in the failure of the algorithm.

HAssembler.py 	|  Defines the actual baseline assembler and solution assembler functions, as well
				|  some auxillary methods neccasay for their operation.
				|
CGenerator.py   |  Generates random genomes, as well as loads existing genomes from text files in
				|  raw format.
				|
Test.py 		|  Defines useful structure for testing and comparing the operation of the baseline
				|  and solutions methods

readsToHaps defines the baseline algorithm; rTH defines the optomized version. Testing methods are provided for convienance and clarity of use. For example, to run a comparision test over a random genome of length 10k with read length 100:

import Test
Test.trial(10000,100)

This gives the following example output:

3.32129096985 True | 0.682209014893 True
^				^         	^			^
baseline 		base 		opt 		opt
runtime 		correct 	runtime		correct

• randomGenome(filename,length)
• mutate(gene)
• makeHaplotypes(filename)
• makeReads(filename, read_length)

• unique_sorted(values)
• getReads(filename)
• getReference(filename)
• compareStrings(reference,read)
• alignReadOld(filename,read)
• prettySNPS(str1,str2)
• readInRange(start,end,read)
• readsOverlap(last_read, next_read)
• readToSNPS(reference,read,offset)
• SNPSToHaplotypes(read_in)
• readsToSNPS(filename)
• makeCacheOld(filename,cacheLen)
• chainsWithinOne(chain)
• chainsWithinK(chain,k)
• withinDistance(cache,key,distance)
• makeCache(reference_genome,cacheLen)
• rTH(filename,cache_len)
• rTHOptOld(filename)
• readsToHaps(filename)

• runBase(g_length,r_length)
• runOpt(g_length,r_length)
• test()
• trial(g_length,r_length)